Analysis and Optimization Study of Piston in Diesel Engine Based on ABC-OED-FE Method

In order to increase the reliability and service life of piston in a heavy-duty diesel engine, the geometric structure of piston was optimized based on its maximum temperature and maximum coupling stress. To begin with, the boundary conditions of thermal and stress fields are calculated, which include the heat produced by the combustion in cylinder, the friction-induced heat, and the heat transferred to cooling system. Then, the finite element model was established to calculate and analyse the temperature and thermal-mechanical coupling stress fields of the piston. By combining this simulation model with orthogonal experimental design methods, computations and analyses were performed to determine how the five geometric parameters (depth of intake and exhaust valve grooves, radius of valve grooves transition, radius of top of valve grooves, height of first piston ring groove, and depth of piston ring groove) influence the two evaluation indicators (maximum temperature and maximum stress of piston). Subsequently, using the proposed ABC-OED- FE (artificial bee colony, orthogonal experiment design, and fitting equations) method, the fitting equations between the geometric parameters and evaluation indicators were determined. Taking the minimum values of two evaluation indicators of piston as optimization objectives, artificial bee colony method was run to determine the values of parameters. At last, the two evaluation indicators of the optimized piston were computed. The results indicate that, after optimization, the maximum temperature of piston decreases to be 16.05 K and the maximum stress decreases to be 13.54 MPa. Both temperature and stress conditions of the optimized piston had been improved, which demonstrates the effectiveness of the optimization and the validity of the algorithm.

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The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/3718174 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Yi Dong ◽

Jianmin Liu ◽

Yanbin Liu ◽

Xinyong Qiao ◽

Xiaoming Zhang ◽

...

Keyword(s):

Finite Element ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Maximum Temperature ◽

Grey Wolf ◽

Neuron Network ◽

Bee Colony ◽

Comparison Results ◽

Grey Wolf Algorithm ◽

The Way

In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement.

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Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078718000247 ◽

2018 ◽

Vol 10 (4) ◽

pp. 437-445 ◽

Cited By ~ 3

Author(s):

Chao Yang ◽

Lixin Guo

Keyword(s):

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Orthogonal Experimental Design ◽

Sea Clutter ◽

Particle Swarm Optimizer ◽

Bee Colony ◽

Opposition Based Learning ◽

Atmospheric Duct ◽

Comparison Results ◽

Better Than

AbstractIn this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

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Application of Improved Artificial Bee Colony Algorithm to the Parameter Optimization of a Diesel Engine With Pilot Fuel Injections

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4036766 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 9

Author(s):

Qiang Zhang ◽

Ryan M. Ogren ◽

Song-Charng Kong

Keyword(s):

Diesel Engine ◽

Nitrogen Oxide ◽

Artificial Bee Colony ◽

Exhaust Gas Recirculation ◽

Exhaust Gas ◽

Abc Algorithm ◽

Bee Colony ◽

Main Injection ◽

Pilot Fuel ◽

Gas Recirculation

Modern diesel engines are charged with the difficult problem of balancing emissions and efficiency. For this work, a variant of the artificial bee colony (ABC) algorithm was applied for the first time to the experimental optimization of diesel engine combustion and emissions. In this study, the employed and onlooker bee phases were modified to balance both the exploration and exploitation of the algorithm. The improved algorithm was successfully trialed against particle swarm optimization (PSO), genetic algorithm (GA), and a recently proposed PSO-GA hybrid with three standard benchmark functions. For the engine experiments, six variables were changed throughout the optimization process, including exhaust gas recirculation (EGR) rate, intake temperature, quantity and timing of pilot fuel injections, main injection timing, and fuel pressure. Low sulfur diesel fuel was used for all the tests. In total, 65 engine runs were completed in order to reduce a five-dimensional objective function. In order to reduce nitrogen oxide (NOx) emissions while keeping particulate matter (PM) below 0.09 g/kW h, solutions call for 43% exhaust gas recirculation, with a late main fuel injection near top-dead center. Results show that early pilot injections can be used with high exhaust gas recirculation to improve the combustion process without a large nitrogen oxide penalty when main injection is timed near top-dead center. The emission reductions in this work show the improved ABC algorithm presented here to be an effective new tool in engine optimization.

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Effects of Piston-Ring Dynamics on Ring/Groove Wear and Oil Consumption in a Diesel Engine

10.4271/970835 ◽

1997 ◽

Cited By ~ 16

Author(s):

Tian Tian ◽

Remi Rabute ◽

Victor W. Wong ◽

John B. Heywood

Keyword(s):

Diesel Engine ◽

Piston Ring ◽

Oil Consumption ◽

Ring Groove ◽

Ring Dynamics

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Finite Element Analysis of the Piston Thermal Load in a Diesel Engine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.459.304 ◽

2013 ◽

Vol 459 ◽

pp. 304-309

Author(s):

Qing Ping Zheng ◽

Chun Yan Ma ◽

Jie Zhong Zhang

Keyword(s):

Finite Element Analysis ◽

Finite Element ◽

Diesel Engine ◽

Thermal Load ◽

Thermal Strain ◽

Three Dimensional ◽

Maximum Temperature ◽

Ring Groove ◽

Element Analysis ◽

Three Dimensional Modeling

Three-dimensional modeling and finite element analysis on the diesel engine piston is carried out in the paper. The distribution of temperature, stress and strain within piston at the rated conditions of the engine are obtained from the simulation. The calculated temperature is consistent with the results of the piston surface temperature which is obtained by hardness plug method, thus confirming the model's validity. The calculated maximum temperature is 374 °C and the minimum temperature is 144 °C. The maximum stress is 118MPa located between the piston skirt above the pin hole and the third ring groove. The maximum thermal strain appears at the piston top with the value of 6.29×10-3. Finally, the temperature simulation of the piston adopted oil-splashing cooling is implemented. It is proved that thermal load can be further reduced through cooling measure.

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Thermal Based Optimum Design of Tapered Roller Bearing Through Evolutionary Algorithm

ASME 2013 Gas Turbine India Conference ◽

10.1115/gtindia2013-3792 ◽

2013 ◽

Cited By ~ 3

Author(s):

Rajiv Tiwari ◽

Rahul Chandran

Keyword(s):

Evolutionary Algorithm ◽

Optimum Design ◽

High Speed ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Roller Bearing ◽

Maximum Temperature ◽

Dynamic Capacity ◽

Bee Colony ◽

Tapered Roller

In high-speed applications the maximum temperature in bearings are a crucial concern. In some applications the bearing is the prime source of heat, the temperature at which a bearing operates dictates the type and amount of lubricant and the material for the fabrication of the bearing components. In the present work a thermal based optimum design of tapered roller bearings has been presented. Internal geometry of the bearing has been optimized based by evolutionary algorithm. Constraints are geometrical, kinematical, strength and thermal in nature. Optimum designs have been found to have better performance parameters. Artificial bee colony algorithm has been used for the present optimization problem, for solving constrained non-linear optimization formulations. A total of nine design variables corresponding to the bearing geometry and constraint factors have been considered. A convergence study has been carried and optimum designs based on temperature is compared with the optimized values based on dynamic capacity, both using artificial bee colony algorithm. There is an excellent improvement found in the optimized bearing designs based on temperature when compared with the optimized results based on dynamic capacity in respect of the maximum temperature in the bearing with the artificial bee colony algorithm.

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